This Python Checkers AI course is designed for developers and AI enthusiasts who want to understand how artificial intelligence is applied to board games. The course begins by explaining the Minimax algorithm in a clear and practical way, showing how decision trees and game states are evaluated. You will learn how AI chooses the best possible move by simulating future game scenarios. In the implementation section, the course demonstrates how to apply Minimax to a Checkers game, including board evaluation, move generation, and turn management. Visualization techniques are also covered to help you see how the algorithm explores different moves and makes decisions. Throughout the course, real coding examples guide you step by step, making complex AI concepts easier to understand. By the end of this course, you will have a working Checkers AI built in Python and a strong foundation in game AI logic. This course is ideal for intermediate to advanced Python developers, students, and anyone interested in learning artificial intelligence through practical game-based projects.